Week 4: Visualizing Time Series and Trends

Emorie D Beck

Time Series

What is a time series?

“a series of values of a quantity obtained at successive times, often with equal intervals between them”

What is a time series?

# A tibble: 100 × 2
       t value
   <int> <dbl>
 1     1     5
 2     2     5
 3     3     3
 4     4     4
 5     5     4
 6     6     4
 7     7     4
 8     8     3
 9     9     3
10    10     2
11    11     3
12    12     4
13    13     4
14    14     3
15    15     3
# … with 85 more rows

Who should care about time series?

People who:

  • study longitudinal change (e.g., development)

  • study variability (e.g., experience sampling, passive sensing)

  • run experiments with multiple trials

  • study cohort or age differences

  • simulations (e.g., trace plots in bayesian models)

  • Time is everywhere, and ignoring it can be problematic

What will we cover with time series:

  • Univariate time series
  • Multivariate time series
  • Connected scatter plots
  • Smoothing
  • Detrended time series

This isn’t the first time we’ve seen time series, but today we’ll focus on telling stories with time series

Univariate and Multivariate Time Series

Why visualize a time series if you don’t care about the trend?

  • This is another way to describe your data that can make sure that you see if something went wrong

Univariate and Multivariate Time Series

  • How you visualize the trends you are trying to uncover in a time series will depend on the research question you are asking
    • e.g., very basic time series visualizations are great for descriptives
    • But to include it in a presentation / papers, we usually want to add more affordances that clarify nothing went wrong
    • Affordances include, text, shading, and more, in aligment with Gestalt principles and how we process different aspects of visualizations